Lab 13 Homework
Gabriel Jones
2023-03-06
Instructions
Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above. For any included plots, make sure they are clearly
labeled. You are free to use any plot type that you feel best
communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(tidyverse)
library(janitor)
library(here)
library(ggmap)Load the Data
We will use two separate data sets for this homework.
The first data set represent sightings of grizzly bears (Ursos arctos) in Alaska.
The second data set is from Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
Load the
grizzlydata and evaluate its structure.
grizzly <- read.csv("data/bear-sightings.csv")
glimpse(grizzly)## Rows: 494
## Columns: 3
## $ bear.id <int> 7, 57, 69, 75, 104, 108, 115, 116, 125, 135, 137, 162, 185, …
## $ longitude <dbl> -148.9560, -152.6228, -144.9374, -152.8485, -143.2948, -149.…
## $ latitude <dbl> 62.65822, 58.35064, 62.38227, 59.90122, 61.07311, 62.91605, …- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
grizzly %>%
select(latitude, longitude) %>%
summary()## latitude longitude
## Min. :55.02 Min. :-166.2
## 1st Qu.:58.13 1st Qu.:-154.2
## Median :60.97 Median :-151.0
## Mean :61.41 Mean :-149.1
## 3rd Qu.:64.13 3rd Qu.:-145.6
## Max. :70.37 Max. :-131.3bbox <- make_bbox(c(-166.2, -131.3), c(55.02, 70.37), f=0.05)- Load a map from
stamenin a terrain style projection and display the map.
map1 <- get_map(bbox, maptype = "terrain", source = "stamen")## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.ggmap(map1)
- Build a final map that overlays the recorded observations of grizzly bears in Alaska.
ggmap(map1)+
geom_point(data=grizzly, aes(longitude, latitude), size = 1)+
labs(title = "Grizzly Bear Sighting Locations in Alaska",
x= "Longitude",
y="Latiude")
Let’s switch to the wolves data. Brandell, Ellen E (2021), Serological dataset and R code for: Patterns and processes of pathogen exposure in gray wolves across North America, Dryad, Dataset.
- Load the data and evaluate its structure.
wolves <- read.csv("data/wolves_data/wolves_dataset.csv")
glimpse(wolves)## Rows: 1,986
## Columns: 23
## $ pop <chr> "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "AK.PEN", "…
## $ year <int> 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 200…
## $ age.cat <chr> "S", "S", "A", "S", "A", "A", "A", "P", "S", "P", "…
## $ sex <chr> "F", "M", "F", "M", "M", "M", "F", "M", "F", "M", "…
## $ color <chr> "G", "G", "G", "B", "B", "G", "G", "G", "G", "G", "…
## $ lat <dbl> 57.03983, 57.03983, 57.03983, 57.03983, 57.03983, 5…
## $ long <dbl> -157.8427, -157.8427, -157.8427, -157.8427, -157.84…
## $ habitat <dbl> 254.08, 254.08, 254.08, 254.08, 254.08, 254.08, 254…
## $ human <dbl> 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10.42, 10…
## $ pop.density <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, …
## $ pack.size <dbl> 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.78, 8.7…
## $ standard.habitat <dbl> -1.6339, -1.6339, -1.6339, -1.6339, -1.6339, -1.633…
## $ standard.human <dbl> -0.9784, -0.9784, -0.9784, -0.9784, -0.9784, -0.978…
## $ standard.pop <dbl> -0.6827, -0.6827, -0.6827, -0.6827, -0.6827, -0.682…
## $ standard.packsize <dbl> 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3157, 1.3…
## $ standard.latitude <dbl> 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7214, 0.7…
## $ standard.longitude <dbl> -2.1441, -2.1441, -2.1441, -2.1441, -2.1441, -2.144…
## $ cav.binary <int> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ cdv.binary <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ cpv.binary <int> 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, …
## $ chv.binary <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ neo.binary <int> NA, NA, NA, 0, 0, NA, NA, 1, 0, 1, NA, 0, NA, NA, N…
## $ toxo.binary <int> NA, NA, NA, 1, 0, NA, NA, 1, 0, 0, NA, 0, NA, NA, N…- How many distinct wolf populations are included in this study? Make a new object that restricts the data to the wolf populations in the lower 48 US states.
length(unique(wolves$pop))## [1] 17unique(wolves$pop)## [1] "AK.PEN" "BAN.JAS" "BC" "DENALI" "ELLES" "GTNP" "INT.AK"
## [8] "MEXICAN" "MI" "MT" "N.NWT" "ONT" "SE.AK" "SNF"
## [15] "SS.NWT" "YNP" "YUCH"lower_us <- wolves%>%
filter(pop != "AK.PEN" & pop != "INT.AK" & pop != "SE.AK")
head(lower_us)## pop year age.cat sex color lat long habitat human
## 1 BAN.JAS 2001 A F B 52.19775 -117.0543 18553.18 1144.68
## 2 BAN.JAS 2003 A F B 52.19775 -117.0543 18553.18 1144.68
## 3 BAN.JAS 2001 A F B 52.19775 -117.0543 18553.18 1144.68
## 4 BAN.JAS 2003 A F B 52.19775 -117.0543 18553.18 1144.68
## 5 BAN.JAS 2005 S M B 52.19775 -117.0543 18553.18 1144.68
## 6 BAN.JAS 2001 A F G 52.19775 -117.0543 18553.18 1144.68
## pop.density pack.size standard.habitat standard.human standard.pop
## 1 8.85 9.56 0.7667 -0.4298 -0.5925
## 2 8.85 9.56 0.7667 -0.4298 -0.5925
## 3 8.85 9.56 0.7667 -0.4298 -0.5925
## 4 8.85 9.56 0.7667 -0.4298 -0.5925
## 5 8.85 9.56 0.7667 -0.4298 -0.5925
## 6 8.85 9.56 0.7667 -0.4298 -0.5925
## standard.packsize standard.latitude standard.longitude cav.binary cdv.binary
## 1 1.7742 0.1928 -0.0102 NA 1
## 2 1.7742 0.1928 -0.0102 NA 1
## 3 1.7742 0.1928 -0.0102 NA 1
## 4 1.7742 0.1928 -0.0102 NA 1
## 5 1.7742 0.1928 -0.0102 NA 0
## 6 1.7742 0.1928 -0.0102 NA 1
## cpv.binary chv.binary neo.binary toxo.binary
## 1 1 NA NA NA
## 2 1 NA NA NA
## 3 1 NA NA NA
## 4 1 NA NA NA
## 5 1 NA NA NA
## 6 1 NA NA NA- Use the range of the latitude and longitude to build an appropriate bounding box for your map.
lower_us%>%
select(lat, long)%>%
summary()## lat long
## Min. :33.89 Min. :-151.06
## 1st Qu.:44.60 1st Qu.:-117.05
## Median :46.83 Median :-110.99
## Mean :49.77 Mean :-113.97
## 3rd Qu.:57.89 3rd Qu.:-110.55
## Max. :80.50 Max. : -82.42bbox <- make_bbox(c(-151.06, -82.42), c(33.89, 80.50), f=0.05)- Load a map from
stamenin aterrain-linesprojection and display the map.
map1 <- get_map(bbox, maptype = "terrain", source = "stamen")## ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.ggmap(map1)
- Build a final map that overlays the recorded observations of wolves in the lower 48 states.
ggmap(map1)+
geom_point(data=lower_us, aes(long, lat), size = 1)+
labs(title = "Wolf Observation Locations",
x= "Longitude",
y="Latiude")
- Use the map from #9 above, but add some aesthetics. Try to
fillandcolorby population.
ggmap(map1)+
geom_point(data=lower_us, aes(long, lat, color=pop), size = 1)+
labs(title = "Wolf Observation Locations",
x= "Longitude",
y="Latiude")
Push your final code to GitHub!
Please be sure that you check the keep md file in the
knit preferences.